Computers and Concrete

  • 제9권5호
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  • Pages.375-387
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  • 2012
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Automatic categorization of chloride migration into concrete modified with CFBC ash

  • Marks, Maria (Institute of Fundamental Technological Research, Polish Academy of Sciences) ;
  • Jozwiak-Niedzwiedzka, Daria (Institute of Fundamental Technological Research, Polish Academy of Sciences) ;
  • Glinicki, Michal A. (Institute of Fundamental Technological Research, Polish Academy of Sciences)
  • 투고 : 2010.01.13
  • 심사 : 2012.01.05
  • 발행 : 2012.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this investigation was to develop rules for automatic categorization of concrete quality using selected artificial intelligence methods based on machine learning. The range of tested materials included concrete containing a new waste material - solid residue from coal combustion in fluidized bed boilers (CFBC fly ash) used as additive. The rapid chloride permeability test - Nordtest Method BUILD 492 method was used for determining chloride ions penetration in concrete. Performed experimental tests on obtained chloride migration provided data for learning and testing of rules discovered by machine learning techniques. It has been found that machine learning is a tool which can be applied to determine concrete durability. The rules generated by computer programs AQ21 and WEKA using J48 algorithm provided means for adequate categorization of plain concrete and concrete modified with CFBC fly ash as materials of good and acceptable resistance to chloride penetration.

키워드

참고문헌

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피인용 문헌

  1. Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions vol.77, 2017, https://doi.org/10.1016/j.autcon.2017.01.016
  2. Chloride penetration resistance of concrete containing ground fly ash, bottom ash and rice husk ash vol.13, pp.1, 2014, https://doi.org/10.12989/cac.2014.13.1.017
  3. Diagnosis of Concrete Quality by Structural Analysis vol.1, pp.1, 2012, https://doi.org/10.1520/ACEM20120004
  4. Serviceability Performance Analysis of Concrete Box Girder Bridges Under Traffic-Induced Vibrations by Structural Health Monitoring: A Case Study 2017, https://doi.org/10.1007/s40999-017-0161-3
  5. Prediction of the Chloride Resistance of Concrete Modified with High Calcium Fly Ash Using Machine Learning vol.8, pp.12, 2015, https://doi.org/10.3390/ma8125483
  6. Ecological High Performance Concrete vol.172, 2017, https://doi.org/10.1016/j.proeng.2017.02.186
  7. Prediction of Scaling Resistance of Concrete Modified with High-calcium Fly Ash Using Classification Methods vol.51, 2015, https://doi.org/10.1016/j.procs.2015.05.259
  8. Optimum design of prestressed concrete beams using constrained differential evolution algorithm vol.49, pp.3, 2014, https://doi.org/10.1007/s00158-013-0979-5
  9. Structural optimization of hollow-section steel trusses by differential evolution algorithm vol.16, pp.2, 2016, https://doi.org/10.1007/s13296-016-6013-1
  10. Progress in Artificial Intelligence-based Prediction of Concrete Performance vol.19, pp.8, 2012, https://doi.org/10.3151/jact.19.924